Personalized Generation

Personalized generation aims to tailor AI-generated content—images, text, or other modalities—to individual user preferences using limited examples of their style or data. Current research focuses on improving the accuracy and controllability of these models, employing techniques like model factorization, dual-domain adversarial training, and retrieval-augmented generation with large language models. This field is crucial for advancing applications such as personalized medicine, content creation, and mitigating the risks of misuse in areas like deepfakes and intellectual property infringement.